Paper Info
Title
Randomized RANSAC with T(d, d) test
Abstract
Many computer vision algorithms include a robust estimation step where model parameters are computed from a data set containing a significant pro- portion of outliers. The RANSAC algorithm is possibly the most widely used robust estimator in the field of computer vision. In the paper we show that un- der a broad range of conditions, RANSAC efficiency is significantly improved if its hypothesis evaluation step is randomized. A new randomized (hypothesis evaluation) version of the RANSAC al- gorithm, R-RANSAC, is introduced. Computational savings are achieved by typically evaluating only a fraction of data points for models contaminated with outliers. The idea is implemented in a two-step evaluation procedure. A mathematically tractable class of statistical preverification tests for test sam- ples is introduced. For this class of preverification test we derive an approx- imate relation for the optimal setting of its single parameter. The proposed pre-test is evaluated on both synthetic data and real-world problems and a significant increase in speed is shown.
Year
Venue
Keywords
2002
BMVC
robust estimator,computer vision,synthetic data
Field
DocType
Citations 
Computer vision,Pattern recognition,RANSAC,Computer science,Artificial intelligence
Conference
49
PageRank 
References 
Authors
4.43
10
2
Name
Order
Citations
PageRank
Jiri Matas133535.85
Ondrej Chum25677330.20